A constrained clustering approach to duplicate detection among relational data

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4426 LNAI pp. 308 - 319
Issue Date:
2007-12-01
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This paper proposes an approach to detect duplicates among relational data. Traditional methods for record linkage or duplicate detection work on a set of records which have no explicit relations with each other. These records can be formatted into a single database table for processing. However, there are situations that records from different sources can not be flattened into one table and records within one source have certain (semantic) relations between them. The duplicate detection issue of these relational data records/instances can be dealt with by formatting them into several tables and applying traditional methods to each table. However, as the relations among the original data records are ignored, this approach generates poor or inconsistent results. This paper analyzes the characteristics of relational data and proposes a particular clustering approach to perform duplicate detection. This approach incorporates constraint rules derived from the characteristics of relational data and therefore yields better and more consistent results, which are revealed by our experiments. © Springer-Verlag Berlin Heidelberg 2007.
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